Скачать или смотреть Solving the CPLEX not enough memory Issue in CP Optimizer

Learn how to troubleshoot the "not enough memory" error when using CP Optimizer with CPLEX. Discover solutions to optimize memory usage and improve performance in your optimization models.
---
This video is based on the question https://stackoverflow.com/q/67915587/ asked by the user 'minh dang' ( https://stackoverflow.com/u/15970908/ ) and on the answer https://stackoverflow.com/a/67919961/ provided by the user 'Alex Fleischer' ( https://stackoverflow.com/u/3725596/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: CPLEX not enough memory when usingCP Optimizer

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Troubleshooting the "Not Enough Memory" Error in CP Optimizer

If you've been working with IBM's CPLEX to run optimization models, you might have come across the frustrating error stating "not enough memory" during the execution of your CPOptimizer. This can be particularly perplexing when you believe your system is equipped with sufficient resources, such as when you're running on a machine with 16GB of RAM. In this post, we'll dive into the common causes of this issue and offer practical solutions to mitigate memory constraints while using CP Optimizer.

Understanding the Problem

The critical question is: Why does the CPLEX report "not enough memory" even with ample system RAM? The error can arise from various factors, including:

Complex constraints: Your model may contain complex constraints that consume a significant portion of memory.

Memory allocation settings: The way memory is allocated for the workspace may not be optimal.

Parallel processing: Utilizing multiple workers in your optimizer can sometimes lead to higher memory consumption.

Solutions to Optimize Memory Usage

1. Simplifying the Model Constraints

Before making drastic changes to your setup, consider simplifying your constraints. Complex models with many constraints can lead to greater memory demands. Review your model for optimization opportunities:

Reduce redundant constraints.

Consolidate similar ones where possible.

While these actions might require a more in-depth understanding of your specific situation, they are essential in reducing memory usage.

2. Adjusting Memory Allocation

You mentioned using 16000mb of memory for the workspace, which should generally suffice. However, consider changing the CPLEX settings to optimize how memory is allocated. One important adjustment is to reduce the number of concurrent workers as follows:

[[See Video to Reveal this Text or Code Snippet]]

By setting the number of workers to 1, you can potentially free up significant memory. Reducing the workload spread across multiple processes might streamline memory allocation, alleviating some of the pressure on your system.

3. Sharing Data for Effective Community Support

In many cases, reaching out to community forums or support groups can yield helpful insights. Sharing your .dat file (the data file for your model) with others allows them to test alternative configurations that may not lead to memory overload. Often, collaborative troubleshooting can identify optimization techniques tailored to your specific constraints or data structure.

Conclusion

Ultimately, running into memory limitations when using CP Optimizer can be frustrating, but it is often a navigable challenge with the right strategies. By simplifying your model, adjusting memory allocation settings, and engaging with the community you can work towards resolving the "not enough memory" error. With persistence and the right techniques, your optimization process will become smoother and more efficient.

If you have any additional tips or tricks that have worked for you in the past, feel free to share them in the comments!